Autonomous driving technology relies on accurate sensing systems. For example, an autonomous driving vehicle may be equipped with multiple integrated sensors such as one or more cameras, a Light Detection And Ranging (LiDAR), a Radio Detection And Ranging (RADAR) sensors, and sonic and ultra sonic sensors, to capture data such as images/videos, point clouds, vehicle pose information, etc. The autonomous driving vehicle then processes the sensed data to learn information that may aid the control of various vehicle functions. For example, cameras may be used to capture surrounding scenes as the vehicle moves. By processing the captured scene images, the vehicle may learn the objects surrounding it and how far they are. For instance, if the vehicle detects that a pedestrian is about 10 feet in front it, it will control the braking system to apply an emergency braking to stop the vehicle.
However, cameras sensing in the context autonomous driving is challenging. Known problems include e.g., photographic artifacts, overfit field-of-view, aperture, and other camera settings. For example, some of the photographic problems may be lens flares caused by bright light sources. Others may be green rays or “ghosts” caused by self-reflection in a lens. Other problems may include discolorations or over-/under-bright images caused by the CMOS settings. In addition, a single monocular camera can only capture two-dimensional (2D) images but cannot provide depth information of an object. However, depth information is usually critical to autonomous driving vehicles. Although more sophisticated cameras, such as a binocular camera, can provide depth information, they are typically more expensive and therefore increase the cost of the vehicle. Therefore, an improved system for sensing data is needed.
Embodiments of the disclosure address the above problems by a multicamera system.